swMATH ID: 30227
Software Authors: Góra, Grzegorz; Wojna, Arkadiusz
Description: RIONA: A new classification system combining rule induction and instance-based learning. The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to achieve classification accuracy comparable to an algorithm considering the whole learning set. The combination of (k)-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds of domains: more suitable for (k)-NN classifiers and more suitable for rule based classifiers.
Homepage: https://pdfs.semanticscholar.org/ba97/561e8e74da507f0bb310fb3159c49d2f5cae.pdf
Keywords: rule induction; nearest neighbour method; instance-based learning
Related Software: LERS; C4.5; Rseslib; RSES; ROSETTA; ElemStatLearn; ENDER; itsmr; NetKit; TwitterRank; BRENT; SLIQ; DIXER; Guerry; ROSECON; daTac; RSBR_; RRIA; UCI-ml
Cited in: 8 Publications

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